To overcome the mentioned problems, this paper presents a network injected with contextual information (CI-Net). We adopt the dilated residual structure where the dilation operation replaces a part of downsampling layers, guaranteeing large receptive fields and avoiding introducing unnecessary parameters. To fully leverage the provided context of semantic labels, we plug a scene understanding module (SUM) with contextual supervision, which captures the similarity of pixels belong to the same classes and difference of those pertaining to different classes. Specifically, we introduce self-attention block [1]}[2]} to generate attention map and exploit the semantic labels to create the corresponding ground truth. The attention training loss injects the contextual prior into the network, which makes the structure knowing to use correlated features for more accurate prediction. To make these two tasks deeply interacted, we presents two approaches. The first one is that we design a feature sharing module (FSM). Instead of simply adding the task-specific features, we concatenate and put them through a series of downsampling and upsampling operations for more useful representations being obtained. We also devises a consistency loss between the depth and semantic features, forcing them to maintain the intrinsic consistency of first-order relationship.
